Pre-surgical diagnostic tool using biomarkers to evaluate the risk factors of post surgical complications

ABSTRACT

A method of electronically diagnosing a cause of an inflamed and/or painful joint of a patient using a joint specific biological material, the method including: receiving, data regarding tests performed on the joint specific biological material; determining if osteoarthritis (OA) is the cause of the inflamed and/or painful joint based upon one or more of the tests, wherein the diagnosing is based upon a level of cartilage oligomeric matrix protein (COMP) and a ratio of COMP to interleukin-8 (IL-8) in the joint specific biological material; if the one or more of the tests indicate OA is not the cause of the inflamed joint, determining if inflammatory arthritis, crystalline arthritis, rheumatoid arthritis, possible septic arthritis or septic arthritis is the cause of the inflamed joint based upon a further plurality of the tests; and generating a sample results report with result data including diagnosis for use by a clinician.

CLAIM OF PRIORITY

This application is a continuation-in-part of international applicationPCT/US2020/057967, filed on Oct. 29, 2020, which claims the benefit ofU.S. Provisional Patent Application Ser. No. 62/928,114, filed on Oct.30, 2019, and also claims the benefit of U.S. Provisional PatentApplication Ser. No. 63/010,756, filed on Apr. 16, 2020, the benefit ofpriority of each of which is claimed hereby, and each of which isincorporated by reference herein in its entirety.

TECHNICAL FIELD

The disclosure is directed to methods and systems for surgical prognosisprediction based on the detection of biomarkers, measurement ofinflammation, and diagnosis of joint diseases or disorders such asarthritic diseases, infection, and gout.

BRIEF SUMMARY OF BACKGROUND

Total joint arthroplasty (TJA) is the surgical treatment for patientswith arthritis of the knee or hip who experience severe pain andactivity limitations and for whom other treatments have beenunsuccessful. More than 700,000 primary total knee arthroplasties (TKAs)are performed annually in the US, and estimates of TKA are projected toincrease to 673% by 2030. Advances in the last 20 years have allowed TKAto become a reliable and cost-effective procedure for patients with lowrisks for complications. However, TKA is still a resource-intensiveprocedure that can incur significant costs for patients who encounterperi- and/or post-operative complications, such as wound complications,readmissions, systemic or local infections, prosthetic failure, andperiprosthetic joint infection (PJI). Prosthetic failure and PJI oftenrequire revision surgery. Revision surgery is a more complex surgerythan a primary arthroplasty, and complication, morbidity, and mortalityrates are significantly higher in patients that undergo revisionsurgeries. Identifying clinical and joint-specific profiles thatstratify patients based on risks for peri- and post-surgicalcomplications can allow implementation of appropriate risk-basedpre-operative treatments and interventions to reduce these complicationrates.

BRIEF DESCRIPTION OF PROBLEM SOLVED

Currently, tools to stratify patients based on the risk of thelikelihood of post-operative complications have been developed, but theyare in their infancy. These surgical risk prediction tools relypredominantly on clinical measures such as patient medical history,co-morbidities, demographics, and patient-reported outcome measures(PROMs). The predicted capabilities of the risk calculators specific fortotal joint arthroplasties (TJA)s are mediocre, with area under thecurve (AUC) ranging from 0.70 to 0.84, and none of them incorporatedjoint-specific laboratory measures into its calculation algorithm.

The Synovasure® Relative Inflammatory Status Classification (RISC) Panelproposed herein utilizes a comprehensive panel of tests forjoint-specific biomarkers to assess and stratify risks for post-surgicalcomplications based on the differential diagnosis for arthritis. Thebiomarker test results are processed through a decision algorithmcoupled to an electronic interface that identifies and differentiatesbetween six arthritic conditions: isolated idopathic osteoarthritis(OA), inflammatory OA, rheumatoid arthritis (RA), Calcium PyrophosphateDihydrate (CPPD) Crystal Deposition Disease (Pseudogout), Monosodiumurate (MSU) crystals (Gout), and septic arthritis (SA). The electronicalgorithm also connects the biomarker results and arthritic diagnosis toa relative inflammatory score of 0-IV, with 0 being none to mildinflammation that is predictive of low post-surgical complication risksto IV being highest in inflammation, complications, and risks formorbidity and mortality. In combination with patient clinical measures,the relative inflammatory score will serve as a predictive factor intothe prognostic classification machine learning (ML) algorithm thatdetermines post-operative complication risks from TJA.

OVERVIEW

There is a significant unmet medical need for an accurate predictivetool that can calculate a patient's risk for peri- and post-surgicalcomplications for TJA. The use of joint-specific clinical biomarkerresults in combination with patient medical record data will provide acomprehensive assessment of the risk factors to accurately predict therisks prior to surgery. Results from this risk prediction tool can beused to guide medical treatment pathways specific for the patient.However, the diagnostics and predictive tools proposed herein arecomplicated and should be completed with the aid of a machineimplemented tool. Put another way, the battery of tests, analysis of thetests, determination of outcome of such test, results presentation andtreatment plan(s) would not be performed by a clinician unaided by amachine implemented tool.

The present inventors have developed a machine implemented panel usingbiomarkers, compositions, algorithms, and machine implemented methods toaid clinicians in clarifying the differential diagnosis ofosteoarthritis, rheumatoid arthritis, crystalline arthritis, andinfectious arthritis in synovial fluid of patients experiencing jointpain and/or inflammation, and ensure the possibility of alternative oradditional diagnoses are evaluated, particularly in cases where theclinical presentation may not be clear. Biomarkers, compositions,algorithms, and machine implemented methods of the present patentdisclosure enable a valid and complete diagnosis of arthritic type alongwith inflammation level that, along with medical history, is predictiveof peri- and post-surgical complications.

Accurate and complete diagnosis and risk prediction provide the bestfoundation for informing treatment decision and treatment success.Biomarkers, compositions, algorithms, and methods of the present patentdisclosure provide a valid and complete differential diagnosis of themost common sources of unspecified joint pain and/or inflammation, e.g.whether due to OA, RA, CPPD, MSU, SA, or a combination of two or more ofthese disorders, thereby giving clinicians the information necessary forselection of the pharmacological, surgical and other interventions thatare most appropriate and helpful to treating the specific disease thatinflicts the patient. Thus, an objective of the present application isto identify biomarkers and arthritic diagnosis that corresponds to arelative joint inflammation level that, when used in combination withpatient clinical measures, can predict surgical or treatment successesand/or complications.

Furthermore, the present inventors contemplate that results from themachine implemented panel based upon a pre-operative patient-specificjoint health result (such as a joint specific biomarker) indicative ofthe level of inflammation in the joint can be used as objective input toa broader patient risk stratification tool so as to better predictpost-operative outcome, and thereby, reduce unnecessary health spendingand provide the clinician (and patient) with a more pro-active,personalized pre- and post-surgical treatment plan.

SUMMARY AND WORKING EXAMPLES

This summary is intended to provide an overview of subject matter of thepresent patent application. It is not intended to provide an exclusiveor exhaustive explanation of the invention or its full scope or all itsfeatures. The detailed description is included to provide furtherinformation about the present patent application.

The present inventors have recognized that a valid differentialdiagnosis of arthritis due to joint pain and/or joint inflammation canbe performed by analyzing a sample from the patient. Furthermore, thedifferentiated arthritic diagnosis can be linked to a joint-specificinflammation level that, in combination with patient medical history anddemographics, is predictive of post-surgical prognosis. Post-surgicalprognosis can include all criteria defined as complications or adverseevents by the. Knee Society[1], which includes but not limited torecovery time, wound healing, infection, re-admission, loss of mobility,implant loosening or dissociation, reoperation or revision, and death.(1. Healy, W. L., et al., Complications of total knee arthroplasty:standardized list and definitions of the Knee Society. Clinicalorthopaedics and related research, 2013. 471(1): p. 215-220.) Theanalysed sample can be a joint specific biological material (e.g., acartilage of the joint, a synovial fluid of the joint, or the like) orother biological fluid such as blood, urine, or cerebrospinal fluid(CSF).

The presence of and/or the levels of one or a combination of biomarkersin the sample can be used to determine the arthritic type, inflammationlevel, and the risk factor for post-surgical complications. The methods,algorithms, systems, and compositions disclosed herein are useful indiagnosing and stratifying patients based on risks for post-surgicalcomplications in the treatment of arthropathy. To further illustrate thecompositions, combinations, methods, algorithms, and systems disclosedherein, a non-limiting list of Working Examples of the inventionprovided here:

Working Example 1 can include the diagnosis of primary, idiopathicosteoarthritis, which is indicative of none to low level of jointinflammation and, in the absence of any other pre-existing patientmedical history risk factors, can be predictive of very low risk forpost-surgical complications. Case scenario can include the following:patient presents to clinician for consultation regarding a kneereplacement surgery. The physician provides the medical assessment,reviews the patient medical history and demographic information, andfinds the patient to be a suitable surgical candidate. The physicianorders the Synovasure® RISC™ Panel to evaluate joint-specific riskfactors. The RISC Panel Results Report show synovial fluid COMPconcentration of above 1500 ng/mL and the COMP/IL-8 result of above 4.3ng/pg. All other panel components (RF, anti-CCP, crystals, WBC, and %PMN) are not present or not elevated to the level of the clinicaldecision limit that is indicative of an inflammatory type of arthritis.The test results along with details of the patient's medical history areprocessed through an algorithm coupled to an electronic interface thatprovides the comprehensive diagnosis of primary, idiopathicosteoarthritis, which corresponds to a RISC Class level of 0, suggestinglow to no inflammation present in the affected joint. Based on thejoint-specific inflammatory profile and patient risk level results fromthe Synovasure® RISC Panel algorithm (which can be the primary factorconsidered) and patient medical history, the physician determines thatthe patient remains a good surgical candidate and recommends patient forpre-, peri-, and post-operative treatment pathway designed for low-riskpatients. This treatment pathway can include selecting for lower-costoptions such as ambulatory surgery center (ASC), non-robotic assistedsurgery, standard implant prosthesis, and an elimination or reducedpreoperative prophylactic administration of antibiotics.

Working Example 2 can include the diagnosis of mildly inflamed,non-differentiated arthritis, and in the absence of any otherpre-existing patient medical history risk factors is predictive of lowlevel of risk for post-surgical complications. Case scenario can includethe patient medical assessment, medical history, demographics, and thefollowing synovial fluid RISC Panel test results: COMP levels of above1500 ng/mL with COMP/IL-8 result of below 4.3 ng/pg. RF, anti-CCP,crystals, WBC, and % PMN are not present or not elevated to the level ofthe clinical decision limit. The test results and selected items fromthe patient medical history and demographics are processed through analgorithm coupled to an electronic interface that provides thecomprehensive diagnosis of mildly inflamed, non-differentiatedarthritis, which corresponds to a RISC Class level of I, suggesting lowlevel of inflammation present in the affected joint. Based on thejoint-specific inflammatory profile, and patient medical history andpatient risk level results from the Synovasure® RISC Panel algorithm(which can be primary factor considered), the physician determines thatthe patient remains a good surgical candidate and recommends patient forpre-, peri-, and post-operative treatment pathway designed for low-riskpatients. However, to further reduce the risk of post-surgicalcomplications, the physician prescribes a 4-month preoperative diet andactivity plan that includes smoking cessation to further reduceinflammation. The surgical treatment pathway can include selecting forlower-cost options such as ambulatory surgery center, non-roboticassisted surgery, standard implant prosthesis, and reduced preoperativeprophylactic administration of antibiotics.

Working Example 3 can include the diagnosis of rheumatoid arthritis,which is indicative of moderate level of joint inflammation and, in theabsence of any other pre-existing patient medical history risk factors,is predictive of medium risk for post-surgical complications. Casescenario can include the patient medical assessment, medical history,demographics, and the following synovial fluid RISC Panel test results:COMP levels of below 1500 ng/mL OR COMP levels of above 1500 ng/mL withCOMP/IL-8 result of below 4.3 ng/pg, positive for anti-CCP AND RF levelsof above 10 IU/mL. Crystals, WBC, and % PMN are not present or notelevated to the level of the clinical decision limit. The test resultsalong with selected information from the patient's medical history areprocessed through an algorithm coupled to an electronic interface thatprovides the comprehensive diagnosis of rheumatoid arthritis, whichcorresponds to a RISC Class level of II, suggesting medium level ofinflammation present in the affected joint. Based on the joint-specificinflammatory profile, patient medical history and patient risk levelresults from the Synovasure® RISC Panel algorithm (which can be theprimary factor considered), the physician recommends patient for pre-,peri-, and post-operative treatment pathway designed for medium-riskpatients. The pre-operative treatment plan can include patientoptimization to reduce surgical risks, such as anti-inflammatorypharmacological treatment, weight loss and rest to reduce joint stress,smoking cessation, and education and engagement through interactivedigital applications such as Zimmer Biomet's MyMobility® Platform. Oncepatient optimization is achieved, the peri-operative and post-operativetreatment pathway can include selecting for lower-cost options such asprocedures performed in an outpatient setting at an ambulatory surgerycenter, non-robotic assisted surgery, standard implant prosthesis,reduced preoperative prophylactic administration of antibiotics, andstandard post-operative care. If patient optimization is not achieved,then the peri-operative pathway may be escalated to usingrobotic-assisted surgery such as the Zimmer Biomet ROSA® system,personalized fitted prosthesis such as the Zimmer Biomet PersonaIQ®smart knee implant, and prophylactic antibiotic administration.Post-operative care pathway can include post-operative antibioticadministration, physical therapy, and tracking of motion data from thePersonaIQ® smart knee implant.

Working Example 4 can include the diagnosis of Gout, which is indicativeof moderate to high level of joint inflammation and, in the absence ofany other pre-existing patient medical history risk factors, ispredictive of medium to high risk for post-surgical complications. Casescenario can include the following example: A 53-year-old male presentsin office with a swollen right knee. He is complaining aboutexcruciating pain upon activity. Also has a history of self-diagnosedbouts of anterior tibialis pain that has been previously managed with awalking boot. Patient confirms that he occasionally, approximately 3times a week, consumes alcohol in the form of beer and wine. Radiographswere taken of the knee, there appears to be adequate joint space andthere is no obvious appearance of sclerosis or osteophyte formation. Tomanage the acute symptoms a dose of corticosteroid was administered viainjection into the capsule. Prior to administration of thecorticosteroid approximately 15 cc of joint fluid was aspirated from theaffected knee and sent for analysis using the RISC panel. The specimenwas first reviewed for integrity and the absorbance at 280 nm was withinthe upper and lower bounds. Additionally, the RBC count was below180,000 cells/microliter. The biomarker analysis revealed a COMP levelof 3450 ng/ml and a COMP to IL-8 result of 1.5 ng/pg. Further analysisshowed a total nucleated cell count of 2800 cells/microliter with adifferential of 65% neutrophils and 5% mononuclear cells. Rheumatoidfactor in synovial fluid was 4 IU/ml and Anti-CCP levels were negative.Crystal analysis revealed the presence of Monosodium Urate crystals bothintra- and extracellular. Using the RISC algorithm, the results abovewere negative for the COMP to IL-8 result, RF, and white blood cellcount threshold. However, the sample was positive for the presence ofMSU crystals, which is indicative of Gout. The patient was consulted tochange diet to reduce red meat intake and keep alcohol consumption to aminimum. Additionally, a prescription for febuxostat (Uloric) was alsoprovided to the patient. If lifestyle changes and pharmacologicalintervention fails and the disease has progressed to the stage wheresurgical intervention is needed, the results of the RISC panel andpatient history can be evaluated using an algorithm coupled with anelectronic interface to assess the risk of post-surgical complications.For this patient, based on the patient history and the joint-specificinflammatory level corresponding to the biomarkers present, thephysician determines that the patient exhibits medium to high level ofrisk for post-surgical complications and recommends patient for pre-,peri-, and post-operative treatment pathway designed for medium-highrisk patients. The surgical treatment pathway could include increaseddosing of pharmacologic agents to address gout, robotic-assisted surgerysuch as the Zimmer Biomet ROSA® system, personalized fitted prosthesissuch as the Zimmer Biomet PersonaIQ® smart knee implant, MyMobility®pre- and post-operative patient education and engagement, andprophylactic antibiotic administration. Post-operative care pathway caninclude post-operative antibiotic administration, physical therapy,continuation of the current febuxostsat prescription, dietaryrestrictions, and tracking of motion data from the PersonalQ® smart kneeimplant.

Working Example 5 can include the diagnosis of Pseudogout or CPPD, whichis indicative of medium-high level of joint inflammation and, in theabsence of any other pre-existing patient medical history risk factors,is predictive of medium-high risk for post-surgical complications. Casescenario can include the patient medical assessment, medical history,demographics, and the following synovial fluid RISC Panel test results:COMP levels of below 1500 ng/mL OR COMP levels of above 1500 ng/mL withCOMP/IL-8 result of below 4.3 ng/pg, positive for CPPD crystals.Anti-CCP and RF may or may not present or elevated to the level of theclinical decision limit. WBC and % PMN may or may not be above theclinical decision limit. The RISC Panel Results Report and the patientmedical history are processed through an algorithm using an electronicinterface, and this process confirms the diagnosis of Pseudogout orCPPD. The physician then recommends appropriate pharmacologicalinterventions to reduce inflammation, pain, and occurrence of acuteattacks. If surgery is warranted, based on the joint-specificinflammatory profile and patient risk level, physician recommendspatient for pre-, peri, and post-operative treatment pathway designedfor medium-high risk patients that is similar to Working Example 4above.

Working Example 6 can include the diagnosis of septic arthritis, whichis predictive of high level of risk for post-surgical complications andmorbidity. Case scenario can include the patient medical assessment,medical history, demographics, and/or other presenting symptoms such asacute joint swelling, pain, erythema, warmth, and joint immobility, andthe following synovial fluid RISC Panel test results: COMP levels ofbelow 1500 ng/mL OR COMP levels of above 1500 ng/mL with COMP/IL-8result of below 4.3 ng/pg, WBC count of >3000 cells/μL and/or % PMN >70,positive for NSA Panel (positive for Alpha Defensin and Lactate level of≥70 mg/dL) AND/OR positive for one or more Synovasure® MID tests AND/ORpositive for culture. Anti-CCP, RF, and Crystals may or may not bepresent or elevated to the level of the clinical decision limit. TheRISC Panel test results and patient medical history is processed using adecision algorithm through an electronic interface, and based on theresults of the panel and review of the patient medical history, thephysician confirms the diagnosis of septic arthritis. The physicianorders immediate appropriate antibiotic therapy as well as evacuation ofany present purulent material from the affected joint. Physiciandetermines that the patient is at high risk for post-surgicalcomplications and delays surgery until the infection is cleared. Ifsurgery proceeds after the infection is managed, based on thejoint-specific inflammatory profile, patient history and demographics,and patient risk level, physician recommends patient for pre-, peri, andpost-operative treatment pathway designed for high risk patients. Thistreatment pathway can include choosing to perform robotic-assistedsurgery in the hospital instead of at an ASC and use of prophylacticantibiotics, antibacterial-loaded cement, antimicrobial coated implantsand personalized fitted smart knee implant. Post-surgical care caninclude post-operative antibiotics, extended hospital stay, extendedphysical therapy, and proactive monitoring with MyMobility® andPersonaIQ® applications.

Working Example 7 can include the diagnosis of non-septic,non-differentiated inflamed arthritis, which, in the absence of or incombination of other pre-existing risk factors, is predictive of mediumto high level of risk for post-surgical complications. Case scenario caninclude the patient medical assessment, medical history, demographics,and the following synovial fluid RISC Panel test results: COMP levels ofbelow 1500 ng/mL OR COMP levels of above 1500 ng/mL with COMP/IL-8result of below 4.3 ng/pg, WBC count of >2000 cells/μL and/or % PMN >70,negative for NSA Panel (positive for Alpha Defensin and Lactate level of≥70 mg/dL), negative for all Synovasure® MID tests, negative forculture. Anti-CCP, RF, and Crystals are not present or not elevated tothe level of the clinical decision limit. Based on the joint-specificinflammatory profile from the RISC algorithm interfaced to an electronicsystem and patient medical history, physician recommends patient forpre-, peri-, and post-operative treatment pathway designed for medium-or high-risk patients.

Working Example 8 can include the diagnosis of osteoarthritis withunderlying non-active CPPD, which, in the absence of any otherpre-existing conditions, is predictive of low level of risk forpost-surgical complications. Case scenario can include the patientmedical assessment, medical history, demographics, and the followingsynovial fluid RISC Panel test results: COMP levels of above 1500 ng/mLwith COMP/IL-8 result of above 4.3 ng/pg, positive for CPPD crystals.RF, anti-CCP, MSU crystals, WBC, and % PMN are not present or notelevated to the level of the clinical decision limit. Based on the RISCPanel test results, the physician may determine that the patientexhibits very low joint-specific inflammation and is a good surgicalcandidate that exhibits low risks for post-surgical complications. Basedon the joint-specific inflammatory profile and patient risk level,physician recommends patient for pre-, peri-, and post-operativetreatment pathway designed for low-risk patients.

Working Example 9 use of the invention to determine appropriateness ofpatient for surgical intervention. Case scenario can include: a 5′8″,463 lbs., 45-year-old male patient presents with a red swollen rightknee with a valgus deformity. The patient was having difficultyambulating and specifically climbing stairs. At the time ofpresentation, the knee was swollen to approximately twice the size ofthe contralateral joint and was red and warm to the touch. Radiographsrevealed bone on bone on the medial compartment of the right knee withsignificant osteophyte formation on the posterior medial portion of thetibia. An aspiration of the joint was performed, prior to injection witha cortical steroid. The aspirate was turbid and had a slight brownishappearance. The specimen was sent to CD Laboratories for analysis usingthe RISC panel. The RISC panel results show the following: A₂₈₀ and RBClevels indicate the sample was not diluted or contaminated withperipheral blood. COMP level of 2952 ng/μL, COMP/IL-8 result of 2.7(below the clinical cut-off of 4.3), WBC count of 30000 cells/ILL withdifferential of 86% neutrophils, RF and anti-CCP at below detectionlimit, Alpha Defensin positive, L-Lactate positive, MID positive forCandida, and culture positive for both Candida and StaphlacoccusEpidermis. These results combined with an algorithm were interrogatedusing an electronic interface with the ability to output a risk factorfor complications. These results indicate the patient would be a RISCcategory IV patient and would not be a good candidate for a surgicalintervention with a total joint due to an active infection. The patientwas then scheduled for an open washout of thin affected knee andprescribed a systemic course of antibiotics to address the organismsencountered. Additionally, the patient was recommended to have anutritional assessment and begin modification of his lifestyle or seekgastric bypass surgery to reduce weight prior to becoming a surgicalcandidate.

Further areas of applicability will become apparent from the descriptionprovided herein. The description and specific aspects of the inventionin this overview are intended for purposes of illustration only and arenot intended to limit the scope of the present disclosure.

BRIEF DESCRIPTION OF THE FIGURES

In the figures, which are not necessarily drawn to scale, like numeralsmay describe similar components in different views. Like numerals havingdifferent letter suffixes may represent different instances of similarcomponents. The figures illustrate generally, by way of example, but notby way of limitation, various aspects discussed in the present document.The figures described herein are for illustrative purposes only ofselected examples and not all possible examples or implementations, andthese drawings are not intended to limit the scope of the presentdisclosure.

The following abbreviations shall be used throughout the figures,specification and occasionally in the claims: OA=osteoarthritis;AD=Alpha Defensin; gout=monosodium urate crystals; CPP=calciumpyrophosphate dihydrate crystals; ANTI-CCP=Anti-cyclic citrullinatedpeptide; COMP=Cartilage Oligomeric Matrix Protein; CPPD=CalciumPyrophosphate Deposition; IL-8=Interleukin-8; MID=Microbial ID;MSU=Monosodium Urate; NSA=Native Septic Arthritis; PJI=PeriprostheticJoint Infection; RA=rheumatoid arthritis; RF=Rheumatoid Factor; RBCcount=Red Blood Cell count; WBC count=White Blood Cell count C/O=cut-offlevel of a biomarker to discriminate OA from one or more of the otherdisease states to discriminate a particular stage of OA form anotherstage of OA.

FIG. 1 shows a system and one or more electronic devices upon which oneor more algorithms of the present application can be implemented and/ordisplayed according to an example of the present application.

FIG. 2 shows a technique for identifying one or more arthropathiesafflicting a patient and generating a sample report, treatment planand/or other result sample data for evaluation by a patient riskstratification tool according to an example of the present application.

FIG. 3 shows a system where results from the technique of FIG. 2 aretransmitted as an input to an electronic device that performs furtherrisk assessment and provides a patient outcome determination accordingto an example of the present application.

FIG. 4 illustrates a flowchart showing a technique for determiningpatient outcome implanted using one or more of the systems described inaccordance with an example of this disclosure.

FIG. 5 shows illustrates a block diagram of an example machine uponwhich any one or more of the techniques discussed herein may perform inaccordance with at least one example of this disclosure.

DETAILED DESCRIPTION

Osteoarthritis (OA) is prevalent and results in a significantsocio-economic burden. Osteoarthritis is a progressive degenerativedisease characterized by progressive destruction and loss of articularcartilage, changes to underlying bone and formation of new bone leadingto pain and limitation or ultimately loss of function. Osteoarthritis isa whole joint disease affecting the subchondral bone, synovium,meniscus, ligaments, and periarticular muscles and nerves, in additionto the cartilage. Common signs and symptoms of knee OA includeinflammation, swelling, deformity, tenderness, crepitus (joint crackingor popping), and pain. Osteoarthritis occurs in stages. Once the diseasehas progressed to more severe stages the only recourse is to remove thedamaged joint and replace it with an artificial joint. Diagnosis of OAin earlier stages of the disease would enable treatment, e.g., withhyaluronic acid, autologous protein solution, platelet-rich plasma, stemcells, or disease modifying drugs, before further or irreparable damageis done to the joint.

Knee OA is primarily diagnosed based on clinical signs and symptoms asdiscussed previously. Synovial fluid analysis is infrequently used torule out other conditions in a differential diagnosis. The highprevalence of OA makes it an obvious leading hypothesis for causes ofunspecified knee pain, particularly when paired with an atypicalclinical presentation of an alternative hypothesis and/or x-ray evidenceof joint space narrowing and osteophytes. Under these circumstances, apatient may be misdiagnosed as having OA when they do not actually havethe disease. Alternatively, a patient may be diagnosed with and treatedfor primary OA when, in fact, the OA is either secondary to another typeof arthritis or OA is the primary disease but another type of arthritisis also affecting the joint and should be treated. Inflammatoryarthritis, rheumatoid arthritis, crystalline arthritis (presence ofmonosodium urate crystals and/or calcium pyrophosphate dihydratecrystals), injury/trauma, and/or septic arthritis (joint infection) cancontribute to an inaccurate or incomplete diagnosis of kneepain/inflammation as being due to OA.

Rheumatoid arthritis (RA) is the most common inflammatory arthropathy.Published data indicates a prevalence of secondary OA in 71% of patientswith rheumatoid arthritis (RA). Notably, to conclusively determine thatan OA diagnosis is secondary to RA, the RA diagnosis must have beenpreviously confirmed by presence of anti-cyclic citrullinated peptide(anti-CCP) and/or rheumatoid factor (RF). Secondary OA has beendiagnosed predominantly (68.6%) in patients that are seropositive foranti-CPP. The prevalence of seronegative (anti-CCP and RF) RA at initialpresentation is as high as 50%. In cases of seronegative RA or early RAprior to seropositive (anti-CCP and RF) test results, it is possiblethat OA secondary to undiagnosed RA could be misdiagnosed and treated asprimary OA, particularly when the symptoms and clinical presentation areatypical of the symmetric, inflammatory, peripheral polyarthritisgenerally observed in RA patients. One study found that one fifth of thepatients diagnosed with RA had been misdiagnosed, and nearly two thirdsof these misdiagnosed patients had OA. These misdiagnosed OA patientshad been treated with disease modifying antirheumatic drugs, which hassubstantial clinical health and economic implications.

Gout is a crystal-induced arthritis caused by deposition of themonosodium uric acid (MSU) crystal related to long standinghyperuricemia. It is a common inflammatory arthritis affecting around 5%of the middle-aged and elderly population worldwide. Published dataindicates a possible link between gout and OA pathogenesis. Acuteattacks of gout at individual joints has been associated with thepresence of clinically assessed OA, and the knee joint was identified asa joint where a highly significant association was observed. It isunknown whether OA causes a predisposition to localized deposition ofmonosodium urate (MSU) crystals or if acute attacks of gout andincreased inflammatory mediators in the synovial tissues trigger thepathogenesis of OA. In addition to the known association between MSUcrystal deposition and osteoarthritis, where both conditions exist andmust be treated, the ability to differentiate between an acute attack ofgout and an inflammatory episode of osteoarthritis is necessary toinform treatment decisions. The inflammatory properties which propagatean inflammatory episode of OA may be indicative of an underlyinginflammatory arthritis that has not yet been diagnosed.

Calcium pyrophosphate dihydrate (CPP) crystal deposition disease (CPPD)is the most common cause of articular cartilage chondrocalcinosis (CC).The classification of CPPD includes asymptomatic CPPD, OA with CPPD(formerly known as pseudo-OA), acute CPP crystal arthritis (formerlyknown as pseudogout), and chronic CPP crystal inflammatory arthritis(formerly known as pseudo-RA). CPPD has been reported to be the fourthmost prevalent rheumatic condition after OA, rheumatoid arthritis, andgout. There is a clear association between OA and CPPD, with studiesdemonstrating calcium crystals in the synovial fluid of 30-60% ofunselected OA patients. Unfortunately, the causal relationship betweenOA and CPPD, the impact of calcium crystal deposition on OA diseaseprogression and treatment effects, and the role of calcium crystals inthe synovial inflammation often observed in OA joints remain unanswered.It has been noted that grade of synovial fluid effusion is increased inpatients with CPPD with OA versus OA alone, indicating a moreinflammatory state for joints affected by both conditions. Thisdifference in the inflammatory status may very well impact the naturalprogression rate of the disease and effectiveness of an OA treatment,and so the identification of calcium pyrophosphate crystals in an OAjoint is important when making treatment decisions.

The incidence of septic arthritis (SA), also known as infectiousarthritis, varies from 2 to 10 per 100,000 in the general population to30-70 per 100,000 in patients with rheumatoid arthritis and patientswith joint prostheses. Clinical signs of SA include joint pain,swelling, warmth, and restricted movement. Concomitant septic arthritisin osteoarthritis, rheumatoid arthritis, and crystalline arthritis casesis uncommon but is not rare. A history of rheumatoid arthritis andprevious intraarticular corticosteroid injections are both risk factorsfor septic arthritis. Furthermore, an examination of synovial fluidaspirates found concomitant infection in 5% of samples with crystallinearthritis. Early diagnosis of septic arthritis, as well as prompt andeffective treatment (antibiotics), is essential to avoid irreversiblejoint destruction or even death. The emergent nature of native septicarthritis gives rise to medical guidelines recommending arthrocentesiswith synovial fluid analysis in all patients who have a joint effusionor signs suggestive of inflammation within the joint, without a knowncause.

The biomarkers, compositions, algorithms, and methods disclosed hereinprovide a valid differential diagnosis, including osteoarthritis,inflammatory arthritis, rheumatoid arthritis, crystalline arthritis(gout and CPPD), and native septic arthritis, which correlates with arelative joint inflammation level that, when combined with patientclinical measures, predicts the risks for complications following totaljoint replacement surgery.

Definitions

In describing and claiming the invention, the following terminology willbe used in accordance with the definitions set forth below. Unlessdefined otherwise, all technical and scientific terms used herein havethe same meaning as commonly understood by one of ordinary skill in theart to which this invention belongs. Any methods and materials similaror equivalent to those described herein can be used in the practice ortesting of the present invention. Specific and preferred values listedbelow for radicals, substituents, and ranges are for illustration only;they do not exclude other defined values or other values within definedranges for the radicals and substituents.

As used in this specification and the appended claims, the singularforms “a,” “an,” and “the” include plural references unless the contextclearly dictates otherwise. By way of example, “an element” means oneelement or more than one element. Similarly, references to “the method”includes one or more methods, and/or steps of the type described hereinand/or which will become apparent to those persons skilled in the artupon reading this disclosure and so forth. For the recitation of numericranges herein, each intervening number there between with the samedegree of precision is explicitly contemplated. For example, for therange 6-9, the numbers 7 and 8 are contemplated in addition to 6 and 9,and for the range 6.0-7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5,6.6, 6.7, 6.8, 6.9 and 7.0 are explicitly contemplated.

As used herein, the term “about” means acceptable variations within 20%,of the stated value, such as within 19%, 18%, 17%, 16%, 15%, 14%, 13%,12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2% or 1% of the stated value.

The term “sample” is a biological sample from a patient. This caninclude whole blood, blood plasma, serum, urine, saliva, synovial fluid,synovial tissue, cartilage, muscle, tendon, ligament, and/or otherbodily fluid or tissue. The term “joint specific biological material” isa material withdrawn specifically from an inflamed joint of a patient(e.g., a cartilage of the joint, a synovial fluid, or one of the othermaterials listed above in sample).

The terms “biomarker” and “marker” can be used interchangeably hereinand refer to generally refer to a protein or polypeptide, nucleic acidmolecule, clinical indicator, physiological indicator, a blood cellcount (red blood cell (RBC), white blood cell (WBC), polymorphonuclearcell count(PMN)), or other evidence of a physical or physiologicalcondition or state of a subject that is associated with a disease andthat can be used as a target for analysing samples obtained fromsubjects. Biomarker can encompass proteins or polypeptides themselves aswell as antibodies against same that may be present in a test sample.Proteins or polypeptides used as a marker include any variants andfragments thereof and, immunologically detectable fragments. Proteins orfragments thereof can also occur as part of a complex. Proteins orpolypeptides used as biomarkers according to the present disclosure alsoinclude such complexes. The terms “biomarker” and “marker” alsoencompass nucleic acid molecules comprising a nucleotide sequence thatcodes for a marker protein, and polynucleotides that can hybridize understringent conditions with a part of such nucleic acid molecules. Theterms “biomarker” and “marker” also include “biomarker(s) ofosteoarthritis,” “OA biomarker,” and “biomarker of OA” as definedherein.

As used herein the terms “treat,” “treating,” and “treatment,” meantherapeutic or preventative measures such as those described herein. Themethods of “treatment” employ administration to a patient of a treatmentregimen in order to prevent, cure, delay, reduce the severity of, orameliorate one or more symptoms of the disease or disorder or recurringdisease or disorder. Treatments for osteoarthritis can include, withoutlimitation, one or more of: lifestyle modifications (e.g., weight-loss,exercise to increase muscle strength at the affected joints); physicaltherapy; analgesics, e.g., aspirin, acetaminophen, opioids; oral orinjectable non-steroidal anti-inflammatory drugs (NSAIDs), e.g.,indomethacin, ibuprofen, naproxen, ketoprofen, piroxicam or diclofenac,celecoxib, rofecoxib, valdecoxib, corticosteroids, disease-modifyingosteoarthritis drugs (DMODs), viscosupplementation e.g., hyaluronic acidor hyaluronan (HA), platelet-rich plasma (PRP), cartilage transplant,and total or partial joint replacement surgery. Treatments for RA caninclude, without limitation: physical and/or occupational therapy foraffected joints; nonsteroidal anti-inflammatory drugs (NSAIDs);corticosteroid medications (oral or injectable) e.g., dexamethasone,betamethasone, prednisone; disease-modifying antirheumatic drugs(DMARDs), e.g., methotrexate, leflunomide, hydroxychloroquine andsulfasalazine; biologic response modifying drugs, e.g., abatacept,adalimumab, anakinra, baricitinib, certolizumab, etanercept, golimumab,infliximab, rituximab, sarilumab, tocilizumab and tofacitinib; andsurgery to repair damaged joints, e.g., synovectomy to remove theinflamed lining of the joint (synovium), tendon repair, joint fusionand/or joint replacement. Treatments for monosodium urate crystallinearthritis (gout) can include, without limitation: dietary modifications(gout); oral or injectable NSAIDs; colchicine; corticoids; xantheneoxidase inhibitors (XOIs), including allopurinol and febuxostat;uricosuric agents, e.g., probenecid, fenofibrate, losartan,azapropazone, calcium channel blockers; pegloticase, rasburicase,lesinurad and arthroscopic irrigation. Treatments for calciumpyrophosphate dihydrate disease (CPPD, pseudogout) can include, withoutlimitation; NSAIDs; corticosteroid (oral or injection); colchicine;phosphocitrate; polyphosphate; magnesium carbonate; viscosupplementation(e.g., HA); hydroxychloroquine; methotrexate; biologic responsemodifying drugs, e.g., as listed above; synovectomy; surgery such asarthroscopic irrigation. Treatments for septic arthritis (native septicarthritis and periprosthetic joint infection) can include, withoutlimitation: analgesics; NSAIDs; antibiotics, antifungals and anti-viraldrugs, as appropriate to the nature of the infectious agent. Treatmentsfor trauma/injury can include, without limitation; ice and/or heat;analgesics; NSAIDs; corticosteroids (oral or injection);viscosupplementation (e.g., hyaluronon); PRP; physical therapy;exercise; and surgery.

As used herein the term “comprising,” “having” and “including” and thelike are used in reference to compositions, systems, methods, andalgorithms, and respective component(s) and feature(s) thereof, that arepresent in a given aspect, yet open to the inclusion of one more or moreunspecified elements. The term “including” is used herein to mean, andis used interchangeably with, the phrase “including but not limited to.”

Detecting Biomarkers

The present application incorporates the disclosures of United StatesPatent Application Publication No. 2018/0045737AI and PCT ApplicationPublication No. 2021/087116 (Application Serial No. PCT/US2020/057967)by reference in their entirety.

In one aspect biomarkers can be measured in joint specific biologicalmaterial, e.g., synovial fluid from a reference individual or from asubject experiencing joint pain and/or joint inflammation. In an aspecta biomarker expression profile or biomarker level can be of one or acombination of biomarker polypeptides or proteins (which shall be usedherein interchangeably, and the term protein shall includepolypeptides). In a preferred aspect, biomarkers can be proteins in asynovial fluid sample from a subject experiencing joint pain and/orinflammation. In one aspect, the biomarkers comprise proteins that aredifferentially expressed in different disease states. In an aspect an OAbiomarker can be differentially expressed in the varying stages of thedisease. In one aspect biomarkers can be differentially increased in OA,i.e., the level of an inflammatory biomarker is increased relative to areference, such as normal individual with OA, or an individual with aparticular stage of OA, or an individual with an inflammatoryarthropathy such as RA, CA, septic arthritis, or trauma/injury to ajoint, or a reference from the subject at an earlier time. In otheraspects OA biomarkers can be differentially decreased, i.e., decreasedlevel relative to a reference, such as normal individual without OA, anindividual with a particular stage of OA, or an individual with aninflammatory arthropathy such as RA, CA, septic arthritis, ortrauma/injury to a joint, or a reference from the subject at an earliertime. In one aspect an expression profile of OA biomarkers can compriseat least one OA biomarker that is differentially increased in OA and atleast one OA biomarker that is differentially increased in aninflammatory arthropathy. In one aspect an expression profile of OAbiomarkers can comprise at least one OA biomarker that is differentiallyincreased in OA and at least one OA biomarker that is differentiallydecreased in OA or in a particular stage of OA. These variations inbiomarker profile can also be used to evaluate the likelihood of postsurgical complications, such as re-admission, infection, wound healing,loss of mobility and death.

Osteoarthritis can be detected, diagnosed (including differentialdiagnosis), staged, monitored, and/or treated by determining thepresence and/or level of one or more OA biomarkers in a subject sample.Assessing or detecting the presence and/or level (e.g. a concentration)of expression of any one or a plurality of biomarkers can be performedby any one or any combination of a variety of techniques that are knownin the art. Detection methods that can be employed for detection ofbiomarkers include, without limitation, optical methods, electrochemicalmethods (voltammetry and amperometry techniques), atomic forcemicroscopy, and radio frequency methods, e.g., multipolar resonancespectroscopy. In one aspect assessing or detecting an OA biomarker canbe performed using a combination of known techniques to provide moreaccurate detection of the biomarker (e.g., biochip in combination withmass spectrometry, immunoassay in combination with mass spectrometry,2-D DIGE in combination with mass spectrometry, and any othercombination of known techniques for detecting and/or assessing a levelof a nucleic acid, a polypeptide or protein). Expression of a biomarkercan be assessed in vitro or in vivo in a subject or a reference. Knownmethods and techniques for isolating DNA, RNA and protein, andperforming the methods and techniques disclosed herein can be found anddescribed more detail in standard molecular biology referencepublications, such as: Ausubel et al., (2003) CURRENT PROTOCOLS INMOLECULAR BIOLOGY, John Wiley & Sons, New York, N.Y., CURRENT PROTOCOLSIN MOLECULAR BIOLOGY, John Wiley & Sons, Online ISDN: 1934-3647;Sambrook et al. (1989) MOLECULAR CLONING: A LABORATORY MANUAL, ColdSpring Harbor Press, Cold Spring Harbor, N.Y.; PROTOCOLS USED INMOLECULAR BIOLOGY (eds. Singh, S. K., and Kumar, D., 2020), BenthanScience ISBN: 9789811439292 (available at researchgate.net).

In an aspect a biomarker can be a protein that can be assessed ordetected using several known techniques that can be antibody-based. Inone aspect the level of one or more biomarkers can be detected and/ormeasured by immunoassay. Immunoassay can typically utilize an antibody(or other agent that specifically binds the biomarker or interest) todetect the presence or level of a biomarker in a sample. Antibodies canbe produced by methods well known in the art, e.g., by immunizinganimals with the biomarkers, such as biomarker proteins. Biomarkers canbe isolated from samples based on their binding characteristics.Alternatively, if the amino acid sequence of a polypeptide or proteinbiomarker is known, the polypeptide or protein can be synthesized andused to generate antibodies by methods well known in the art. Further,antibodies are commercially available for biomarkers from many sources(R&D Systems, RayBiotech, EMD Millipore, et.c.). Suitable immunoassaydetection methods for use in the methods and systems disclosed hereininclude, without limitation, Western blot, sandwich immunoassaysincluding enzyme-linked immunosorbent assay (ELISA) and other enzymeimmunoassays, fluorescence-based immunoassays, and chemiluminescence.Other forms of immunoassay include magnetic immunoassay,radioimmunoassay, and real-time immunoquantitative PCR (iqPCR).

In one aspect an ELISA can be used to detect and quantify biomarkerprotein levels. This method can include preparing the antigen (i.e.,biomarker protein of interest), coating the wells of a microtiter platewith the antigen, incubating the antigen with an antibody thatrecognizes the antigen, washing away the unbound antibody, and detectingthe antibody-antigen complex. The antibody can generally be conjugatedto an enzyme, such as horseradish peroxidase or alkaline phosphatase,which can generate colorimetric, fluorescent, or chemiluminescentproducts. In another aspect an ELISA can use two antibodies, one ofwhich is specific to the biomarker protein of interest and the other ofwhich recognizes the first antibody and is coupled to an enzyme fordetection. In still other aspects the antibody can be coated on the welland a second antibody conjugated to a detectable compound is added tothe well following the addition of the antigen to the biomarker proteinof interest.

In another aspect an antibody array platform, e.g., Luminex (LuminexCorp., Austin, Tex.) can be used to detect and quantify biomarkerprotein levels using multiplexed assays based on a capture bead systemin which microsphere beads are color-coded with dyes. Each color-codedbead set is coated with a specific binding reagent such as an antibodyspecific to a selected biomarker protein, allowing the capture anddetection of specific protein analytes from a very small amount offluid, e.g. a drop of fluid from plasma, serum, urine, cells lysates orsynovial fluid. Depending upon the analyte(s) being screened, at leastone or several bead sets may be incubated with a sample to capture theanalytes. In one aspect lasers can be used to excite the dyes thatidentify each microsphere bead and any reporter dye captured during theassay. Exemplary multiplex immunoassay platforms that can be used in thepresent methods, systems and algorithms include the xMAP platform(Qiagen, Inc.).

In an aspect a biomarker protein level can be assessed using a proteinmicroarray or an antibody microarray. In these methods, the proteins orantibodies are covalently attached to the surface of the microarray orbiochip. The biomarker protein of interest can be detected byinteraction with an antibody, and the antibody/antigen complexes aregenerally detected through fluorescent tags on the antibody. Anexemplary microarray that can be used in the methods, systems andalgorithms disclosed herein includes the Quantibody™ platform(RayBiotech, Inc.).

In another aspect biomarker protein levels can be assessed byimmunohistochemistry in which a protein is localized in cells of atissue section by its interaction with a specific antibody. Theantigen/antibody complex may be visualized by a variety of methods. Oneor two antibodies may be used, as described above for ELISA. Thedetection antibody may be tagged with a fluorophore, or it may beconjugated to an enzyme that catalyzes the production of a detectableproduct. The labeled complex is typically visualized under a microscope.

In yet another aspect a biomarker protein level can be measured byWestern blotting. Western blotting generally comprises preparing proteinsamples, using gel electrophoresis to separate the denatured proteins bymass, and probing the blot with antibodies specific to the biomarkerprotein of interest. Detection can be accomplished using two antibodies,the second of which is conjugated to an enzyme for detection or anotherreporter molecule. Methods used to detect differences in protein levelsinclude colorimetric detection, chemiluminescent detection, fluorescentdetection, and radioactive detection.

In one aspect a biomarker protein profile can be assessed byTwo-dimensional difference gel electrophoresis (2D-DIGE). 2D-DIGE is amodified form of 2D electrophoresis (2DE) that allows the comparison oftwo or three protein samples simultaneously on the same gel. Theproteins in each sample can be covalently tagged with different coloredfluorescent dyes that are designed to have no effect on the relativemigration of proteins during electrophoresis. The proteins in the sampleare separated in 2 dimensions using electrophoresis (molecular weight inone dimension; isoelectric point (or net charge) in the seconddimension). When illuminated with appropriate wavelengths of light, thecolor contribution and intensity of individual protein spots indicateswhich sample (disease group) the protein came from. Protein spots ofinterest are cut from the gel and the identity of the protein isdetermined by mass spectrometry.

In one aspect the level of biomarkers can be detected by massspectrometry (MS). Mass spectrometry is a well-known tool for analyzingchemical compounds that employs a mass spectrometer to detect gas phaseions. Mass spectrometers are well known in the art and include, but arenot limited to, time-of-flight, magnetic sector, quadrupole filter, iontrap, ion cyclotron resonance, electrostatic sector analyzer and hybridsof these. The method may be performed in an automated (Villanueva, etal., Nature Protocols (2006) 1(2):880-891) or semi-automated format.This can be accomplished, for example with the mass spectrometeroperably linked to a liquid chromatography device (LC-MS/MS or LC-MS) orgas chromatography device (GC-MS or GC-MS/MS).

In certain aspects biomarkers, e.g., MSU and/or CPP crystals, RBCs,WBCs, can be detected using various forms of microscopy, such as lightpolarizing microscopy and phase contrast microscopy. In one aspectbiomarkers comprising whole cells can be detected and quantified bywell-known manual counting methods and/or automated counting methodsusing automated devices.

In certain aspects biomarkers, e.g., microbial growth andidentification, can be detected using various forms of aerobic andanaerobic microbial culture techniques.

Determinations

FIG. 1 shows a system 100 by which various determinations, diagnosis,categorization and assessment can be carried out including as to thetype of inflammatory arthropathy (e.g., OA, RA, CPPD, MSU, septicarthritis) inflicting a joint. The system 100 can include an electronicdevice 102A and/or electronic device 102B. The electronic device 102Aand the electronic device 102B can include memory, software,communication circuitry, and/or processing circuitry (which may includean integrated circuit, such as a system on a chip, a field-programmablegate array (FPGA), a processor, etc.). The electronic device 102A and/orthe electronic device 102B may be used to generate, store, or send dataas further discussed herein. The electronic device 102A can be a mobiledevice configured to generate or receive data such as a sample resultsreport, diagnosis, treatment plan or the like as further discussedherein.

The system 100 can communicate with a network 104 of other electronicdevices in addition to the electronic devices 102A and 102B. The system100 and network 104 can communicate with various of the testingcomponents/techniques previously discussed (sometime referred to hereinas other system components). The system 100 can include an examplearchitecture and componentry for a computer-implemented system. Theelectronic device 102A and/or the electronic device 102B can include amemory 106 to implement various algorithms. However, the system 100 caninclude a database according to some examples that implements all orportions of the algorithm(s). According to some examples, the electronicdevice 102A and/or the electronic device 102B can be configured as aclient that can run portions or all of the data processing discussedherein. The electronic device 102A and/or the electronic device 102B canbe patient, clinician, insurer, laboratory, manufacturer, or healthcareprovider electronic devices for monitoring and/or collecting datalocally or remotely via the network 104.

The electronic device 102A and/or the electronic device 102B can beassociated with and used for multiple data storage functions. Algorithmsimplemented by the electronic device 102A and/or the electronic device102B may be performed on circuitry (e.g., a processor, software,firmware, hardwired circuitry, etc.) that is capable of performingvarious functions. The electronic device 102A and/or the electronicdevice 102B and/or other system components not specifically shown (e.g.,data repository, server, etc.) can be configured to communicate with oneanother such as via a communication unit and/or can execute functionsalone or in conjunction with one another over the network 104. Theelectronic device 102A can include any number of different portableelectronic mobile devices, including, e.g., cellular phones, personaldigital assistants (PDA's), laptop computers, portable gaming devices,portable media players, e-book readers, watches, as well as non-portabledevices such as desktop computers. The electronic device 102A and/or theelectronic device 102B can include one or more input/output devicesconfigured to allow user interaction with one or more programs. Thus,the electronic device 102A has a display 108 showing data (e.g, sampleresults, diagnosis, treatment recommendation, etc.). In one example, theelectronic device 102A and/or the electronic device 102B may bejettisoned in favor of a web browser that accesses/executes and presentsa web application for use by the user. In another example, theelectronic device 102A and/or the electronic device 102B can execute anapplication outside of a web browser, e.g. an operating system specificapplication that accesses/executes and presents a native OS applicationfor use by the user.

Network 104 can include one or more terrestrial and/or satellitenetworks interconnected to provide a means of communicatively connectingthe electronic device 102A and/or the electronic device 102B and othersystem components. In one example, network 104 can be a private orpublic local area network (LAN) or Wide Area Network (WANs). Network 104can include both wired and wireless communications according to one ormore standards and/or via one or more transport mediums. In one example,network 104 includes wireless communications according to one of the802.11 or Bluetooth specification sets, or another standard orproprietary wireless communication protocol. Network 104 can alsoinclude communications over a terrestrial cellular network, including,e.g. a GSM (Global System for Mobile Communications), CDMA (CodeDivision Multiple Access), EDGE (Enhanced Data for Global Evolution)network. Data such as tests and test results can be transmitted overnetwork 104, e.g., from the various of the testing apparatuses discussedpreviously to the electronic device 102A and/or the electronic device102B via the communication unit. Data can be formatted in accordancewith a variety of different communications protocols. For example, allor a portion of network 104 can be a packet-based, Internet Protocol(IP) network that communicates data in Transmission ControlProtocol/Internet Protocol (TCP/IP) packets, over, e.g., Category 5,Ethernet cables

The memory 106 of the electronic device 102A and/or the electronicdevice 102B (or other system components) can include, e.g., a standardor proprietary electronic database or other data storage and retrievalmechanism. In one example, memory includes one or more databases, suchas relational databases, multi-dimensional databases, hierarchicaldatabases, object-oriented databases, or one or more other types ofdatabases. The memory 106 can be implemented in software, hardware, andcombinations of both. In one example, memory include proprietarydatabase software stored on one of a variety of storage mediums on adata storage server connected to network 104 and configured to storedata such as measured/collected pre-operative sensor data or otherinformation. Storage media included in or employed in cooperation withmemory can include, e.g., any volatile, non-volatile, magnetic, optical,or electrical media, such as a random access memory (RAM), read-onlymemory (ROM), non-volatile RAM (NVRAM), electrically-erasableprogrammable ROM (EEPROM), flash memory, or any other digital media.

The electronic device 102A and/or the electronic device 102B can employthe memory 106 to store and retrieve various types of data, includingbut not limited to tests, test results, data relating to biomarkers,etc. Additionally, the electronic device 102A and/or the electronicdevice 102B can store and retrieve data or other information fromanalytics executed on data, sample results data, as well as otherinformation related to patient population modeling and analysis (e.g.,risk stratification) as further discussed herein.

The system 100 can implement the technique 200 shown in FIG. 2 as analgorithm, or other electronically implemented technique, for example.For the purposes of the technique of FIG. 2 may be referred to aRelative Inflammatory Status Classification (RISC) Panel algorithm.

Diagnosing Joint Pain and/or Inflammation using the Technique of FIG. 2

As shown in FIG. 2, a subject sample can be assessed to determine sampleintegrity as part of the RISC panel 200. The subject sample can beassessed to determine whether the sample has been diluted 212 duringextraction of the sample from the subject, e.g., by saline or bodilyfluid that is not synovial fluid. A determination of dilution 212 can beindicated as a precaution. In an aspect a subject sample can be assessedto determine whether the subject sample is contaminated 214 duringextraction of the sample from the subject, e.g., with, blood, a contrastagent or other agent used during the extraction procedure. In one aspecta subject sample can be assessed using a spectroscopic measurement ofthe sample absorbance 210. In one aspect the spectroscopic absorbance210 of a sample can be measured at 280 nm (A280). In an aspect a subjectsample absorbance can be compared to a reference absorbance, e.g., areference joint specific biological material that is not diluted and isnot contaminated. In one aspect a reference joint specific biologicalmaterial can have an A280 within a range of 0.342 to 1.190 (a normalrange) and absorbance outside of the normal range can indicate thesample is diluted or is contaminated. A subject sample A280 absorbance210 less than 0.342 can indicate a subject sample has been contaminated214 during extraction. In another aspect an A280 absorbance greater than1.190 can indicate the subject sample has been diluted 212 duringextraction. In one aspect an A280 for a subject sample can be determinedby reviewing a report of the subject sample assessment. The results of asample assessment can be provided in an electronic report or a reportcan be automatically generated to indicate the results of the assessmentthat is displayed such as on the electronic device of FIG. 1. In oneaspect a report can indicate a subject sample is contaminated 214 andprovide a cautionary statement that the results of a biomarker assay ofthe sample should be interpreted with caution due to the contaminatedstatus. In another aspect a report can indicate a subject sample isdiluted 212 and include a cautionary statement that the results of abiomarker assay of the sample should be interpreted with caution due tothe diluted status.

A subject sample integrity can be assessed to determine whether thesubject sample is hemorrhagic, e.g., includes an excessive quantity(concentration) of red blood cells (RBCs) 216 or is diluted by blood. Inone aspect a subject sample can be assessed for the quantity or RBCs(RBC count) relative to a reference level. Methods of quantifying RBCsare well known in the art and can include manual counting and automatedcounting methods. In one aspect a reference joint specific biologicalmaterial can have less than 1,000,000 RBCs per microliter, i.e., is nothemorrhagic. In an aspect a subject reference joint specific biologicalmaterial having greater than 1,000,000 RBCs per microliter can beconsidered hemorrhagic 218. In one aspect a subject sample having anA280 within the range of 0.342 to 1.190 (within normal range, notdiluted or contaminated) can be assessed 216 to determine whether thesample has greater than 1,000,000 RBCs per microliter and can beindicated/reported as (hemorrhagic 218). In another aspect a subjectsample that has been determined to be contaminated 214 can be assessedto determine whether the sample has greater than 1,000,000 RBCs permicroliter (hemorrhagic 218). In still another aspect a subject samplethat has been determined to be diluted 212 can be assessed to determinewhether the sample has greater than 1,000,000 RBCs per microliter(hemorrhagic 218). In one aspect an RBC count 216 for a subject samplecan be determined by reviewing an electronic report automaticallygenerated to indicate the results of the assessment. In one aspect areport can indicate a subject sample is classified as hemorrhagic 218and provide a cautionary statement that the results of a biomarker assayof the sample should be interpreted with caution due to the hemorrhagicstatus.

The technique 200 can be a biomarker expression profile can be used todiagnose joint pain and/or joint inflammation, classify, suggesttreatment, predict treatment or surgical prognosis, and be used forinput into a patient risk calculator as further discussed herein. Asample such as a joint specific biological material can be extractedfrom a painful or inflamed joint of a subject. This sample can beassessed by determining a biomarker profile comprising any one or anycombination of biomarkers. The RISC panel algorithm can then be utilizedto determine the cause of the inflamed joint.

In one aspect a biomarker profile can comprise a first OA biomarker(obtained with a first one or more tests). This first OA biomarker canbe, without limitation, COMP at 202, according one example. However,other possible first OA biomarker contemplated include, but are notlimited to AD; HNE; COMP; IL-8; OPN; OPG; OC; Leptin; CRTAC1;Tetranectin; FGF2; TIMP1; TIMP2; IL-8; IL-6; CRP; MMP-3; MMP-9; RANTES;PDGF; and NGAL. In one aspect the first OA biomarker evidencingcartilage degradation is differentially increased in OA relative to areference, e.g. a normal individual.

If the first biomarker (COMP) comes back positive, a second analysis canbe performed to calculate a COMP/IL-8 result at 204. However, anotherresult can be calculated such as COMP/X where X is, without limitation,IL-6, CRP, MMP-9, MMP-3, NGAL). If the result of COMP/IL-8 ≥4.3, thesample is considered positive for OA. If the result of COMP/IL-8<4.3,the sample is considered negative for OA and OA is excluded at step 206in favor or another inflammatory arthropathy such as RA, CA, possibleseptic arthritis or septic arthritis. If the sample is positive for OA,diagnosis of OA can be confirmed at step 208 such as through medicalimaging or other routine technique. A class 0 OA diagnosis can beassigned at 209.

At step 206, further tests are needed to determine the type ofinflammatory arthropathy (RA, CA, possible septic arthritis, or septicarthritis) as OA has been ruled out.

Although 4.3 is used as the example ratio above, other ratios usingother biomarkers are contemplated. Thus, a biomarker ratio of a first OAbiomarker and a second OA biomarker can be compared to a reference ratioof the first OA biomarker and the second OA biomarker. In an aspect abiomarker ratio or a reference result can be based on quantities (e.g.,concentrations) that are adjusted to have like units, such that thelevel of each biomarker is expressed in the result as pg/mL, ng/mL,pg/mL, mg/mL, or mg/dl, or the like such that the units cancel eachother. For example, a synovial fluid sample can have a first OAbiomarker level of 100 ng/mL and a second OA biomarker level of 100pg/mL (or 0.01 ng/mL), providing a biomarker result of 100/0.01 or10,000. In another aspect a biomarker result or a reference ratio can bebased on quantities (e.g., concentrations) that are not adjusted to havelike units, such that a first OA biomarker can have a level expressed inunits that differ from the units of the level of the second OAbiomarker. For example, a first OA biomarker can have a level of 1000ng/mL and the second OA biomarker can have a level of 100 pg/mLproviding a biomarker ratio of 1000/100 or 10. In one aspect a biomarkerratio can be the ratio of a first OA biomarker that is differentiallyincreased in OA and a second OA biomarker that is differentiallyincreased in an inflammatory arthropathy, e.g., differentially decreasedin OA. In an aspect the first OA biomarker can be selected from COMP,OPN, OPG, OC, Leptin, CRTAC1, Tetranectin, FGF2, TIMP1, and TIMP2, and asecond OA biomarker can be selected from IL-8, IL-6, CRP, AD, HNE,MMP-3, MMP-9, NGAL, RANTES or PDGF. In an aspect a biomarker ratiogreater than or equal to 2.0 can diagnose OA or discriminate between OAand an inflammatory arthropathy, e.g., 2.1, 2.2., 2.3, 2.4, 2.5, 2.6,2.7, 2.8, 2.9 and any number therebetween. The biomarker ratio cancomprise a ratio of COMP:IL-8 and a COMP:IL-8 ratio greater than orequal to 3.0 can diagnose idiopathic OA in a subject.

COMP less than or equal to a reference level of COMP in combination withCOMP/IL-Ratio discussed above can exclude OA from a diagnosis. In oneaspect a COMP level less than or equal to 1,500 ng/mL in combinationwith COMP/IL-Ratio discussed above can exclude OA from a diagnosis. Inan aspect a level of COMP less than 4,000 ng/mL in combination withCOMP/IL-Ratio discussed above can exclude OA from a diagnosis. Inanother aspect a synovial fluid level of COMP less than 3,500 ng/mL incombination with COMP/IL-Ratio discussed above can exclude OA from adiagnosis. In one aspect a synovial fluid level of COMP less than 3,000ng/mL in combination with COMP/IL-Ratio discussed above can exclude OAfrom a diagnosis. In one aspect a synovial fluid level of COMP less than2,500 ng/mL in combination with COMP/IL-Ratio discussed above canexclude OA from a diagnosis. In one aspect a synovial fluid level ofCOMP less than 2,000 ng/mL in combination with COMP/IL-Ratio discussedabove can exclude OA from a diagnosis. In one aspect a synovial fluidlevel of COMP less than or equal to 1,500 ng/mL in combination withCOMP/IL-Ratio discussed above can exclude OA from a diagnosis. Inanother aspect a synovial fluid level of COMP less than or equal to1,000 ng/mL in combination with COMP/LL-Ratio discussed above canexclude OA from a diagnosis.

Returning to the biomarker aspects of the RISC Panel technique 200, inan aspect a quantity of white blood cells (WBC), i.e., a WBC count 220,can be determined for a subject sample if OA is excluded at step 206. Inone aspect a WBC count can be a differential WBC count. Methods ofquantifying WBCs are well known in the art and can include manualquantification and automated quantification. In an aspect a differentialWBC count can include the total number of WBCs per volume (WBCconcentration), and the proportion (percentage) of one or more WBC types(e.g., % neutrophils (PMN); % mononuclear cells) relative to the totalWBC quantity. In an aspect a subject sample can be compared to areference e.g., a reference joint specific biological material (e.g.,synovial fluid) for a subject that is known to not have a jointinfection or inflammatory arthropathy. In an aspect a reference jointspecific biological material can have a WBC count 220 with less than orequal to 3,000 WBCs per microliter and/or can have fewer than 70% PMN.In one aspect a subject sample can have a WBC count 220 greater than3,000 cells per microliter suggesting infection, e.g., native jointsepsis/infection or periprosthetic joint infection, and the subjectsample can be further assessed at 222 to determine whether infection ispresent, e.g., culture of the sample and/or assessing biomarkers AD,L-lactate (NSA Panel), and/or Synovasure® Microbial Identificationassays (MID) as described below. In an aspect a subject sample can havea greater than 70% PMN suggesting infection., e.g., native septic jointor periprosthetic joint infection, and the subject sample can be furtherassessed at 222 to determine whether infection is present, e.g., cultureof the sample, assessing biomarkers AD and L-lactate (NSA Panel), and/orMID. In one aspect a WBC count 220 can be determined by reviewing anelectronic report for the subject sample analysis. The results of asample assessment can be provided in the electronic report or theelectronic report can be automatically generated to indicate the resultsof the assessment. In one aspect the RISC panel can include a WBCdifferential count. In one aspect the report generated by the RISC panelfor a subject sample that has greater than 3,000 WBC per microliter orgreater than 70% PMN can include a statement that the subject and/orsubject sample should receive further assessment to determine whetherinfection is present, which can include sample culture and/or additionalbiomarker assessment (e.g., biomarker assessment for AD and L-lactateand/or MID. In one aspect a report for a subject sample that has fewerthan 2,000 WBC per microliter can be classified as a non-inflammatorysample 224 (class I inflammatory arthritis) suggesting that infection oran inflammatory arthropathy are unlikely to be present in the subject'sjoint. In another aspect the electronic report generated by the RISCpanel for a subject sample that has greater than 2,000 WBC permicroliter but less than 3,000 WBC per microliter can be classified asinflammatory sample class III 226 suggesting the likelihood that aninflammatory arthropathy is present without infection. In another aspectthe electronic report generated by the RISC panel for a subject samplethat has greater than 2,000 WBC per microliter but less than 3,000 WBCper microliter with confirmed Gout Diagnosis 248 can be classified asinflammatory sample class IIIA 225 suggesting the likelihood that anactive inflammation induced by MSU crystalline arthropathy is presentwithout infection. In another aspect the electronic report generated bythe RISC panel for a subject sample that has greater than 2,000 WBC permicroliter but less than 3,000 WBC per microliter with confirmed CPPDDiagnosis 250 can be classified as inflammatory sample class IIIB 227suggesting the likelihood that an active inflammation induced by CPPcrystalline arthropathy is present without infection.

Thus, the RISC panel can conduct a WBC count 220, which can indicate thesubject sample has an inflammatory status 222, 224 or 226 according toone or more aspects disclosed above. The WBC count 220 can indicate thesubject sample has an inflammatory status 224 (class I inflammatoryarthritis). When the WBC count 220 indicates likely septic arthritis at222, the sample can be further assessed to make a more accuratedetermination of septic arthritis using the NSA panel 228, the MID 230and/or the culture 232.

Thus, with the RISC panel technique 200, the sample can be furtherassessed for biomarkers AD and L-lactate at NSA Panel 228, MID 230,and/or culture 232 to determine whether the sample was obtained from aninfected joint, i.e., the subject has septic arthritis. In an aspect, ifthe sample that is negative for AD can be confirmed to not have septicarthritis and may be put into class III 226 possible septic arthritis.In another aspect the sample that is positive for AD and that ispositive for L-lactate can be confirmed to have septic arthritis at step234 (Class IV). In an aspect a synovial fluid sample that is positivefor AD and that is negative for L-lactate or has L-lactate level lessthan 70 mg/dL can be considered indeterminate or inconclusive fordiagnosis of septic arthritis and may be put into class III 226 possibleseptic arthritis. In one aspect presence of AD or L-lactate in asynovial fluid sample can be determined by reviewing the electronicreport for the subject sample analysis. The results of a sampleassessment AD and/or L-lactate can be provided in the electronic reportand/or the electronic report can be automatically generated to indicatethe results of the assessment.

Similarly, a positive test MID 230 can result in a diagnosis (Class IVaat 236) of septic arthritis. A positive culture 232 can result in adiagnosis of (Class IVb at 238) of septic arthritis.

In an aspect of the RISC Panel technique, the sample concurrent with WBCcount 220 or after the WBC count 220, can be analyzed for RA and CA atsteps 240 and 242. At step 240 a crystal analysis can be performed forCA. At step 242 a test for RA can be performed.

At step 242, a level of anti-CCP can be determined for the sample. Inone aspect the level of RFs can be determined for sample. In one aspectthe RF level determined for the sample. In one aspect a reference levelof anti-CCP can be greater than or equal to 2 U per milliliter can beindicative of RA at 244. Furthermore, if the sample has RF greater thanor equal to 10 IU per milliliter this can be indicative of RA at 244. RAdiagnosis 244 can be noted and electronically reported andclassification Class IIb at 246 can result. A suggest treatment forsubject can be issued with the electronic report. A negative result forRA using RF and anti-CCP can result in a diagnosis of inflammatorystatus 224 (class I inflammatory arthritis).

As shown in FIG. 2, in one aspect the sample can be assessed for thepresence of crystalline arthritis (CA) at 240. Crystals in a synovialfluid sample can be extracellular or intracellular. In one aspect thecrystals can be MSU crystals at step 248. In another aspect the crystalscan be CPP crystals 250. In an aspect the absence of crystals in thesample at 252 can exclude CA from the diagnosis resulting in thediagnosis of inflammatory status 224 (class I inflammatory arthritis).

In one aspect the presence of MSU crystals in the sample can confirm thepresence of gout at 248 resulting in a Class II categorization at 252.The presence of gout and WBC count of greater than 2000 cells/μL at 220plus negative for NSA at 228, negative for culture at 232, and negativefor MID at 230 can result in a Class IIIa at 225. The presence of CPPcrystals in the sample at 250 can confirm a diagnosis of CPP disease inthe subject and can result in a Class IIa categorization at 254. Thepresence of CPPD and WBC count of greater than 2000 cells/μL at 220 plusnegative for NSA at 228, negative for culture at 232, and negative forMID at 230 can result in a Class IIb at 227.

In an aspect the presence or absence of crystals (and crystal type) inthe sample can be determined and can be electronically reported asresults. The electronic report can indicate the results of theassessment, e.g., the absence of crystals, type of crystals present (MSUor CPP), whether the crystals present are intracellular, extracellular,and/or whether there is/is not an active-flare up of CA.

The RISC panel algorithm can classify the inflammatory severity as“none”, “low”, “medium-high” and “high” for further analysis andscoring.

The RISC panel algorithm not only classifies the inflammatory conditionbut also can evaluate the inflammatory condition of the patientaccording to category (i.e. class 0, class 1, class II, class IIa, classIIb, class III, class IV, class IVa and class IVb). The classificationis be based upon severity of the condition and/or risk to the patient.With the classification increasing with the risk of septic arthritis.Such classification can be used in patient risk assessment and conditionevaluation as further discussed subsequently.

Merely by way of example, a class 0 can have no risk weighting and wouldbe a patient of average joint specific risk (e.g., 1.0) or below averagejoint specific risk with OA suitable for immediate surgery. A class Ipatient would have average to low joint specific risk (e.g., a weightingof 1.0 to 1.2 as compared to a typical patient). Class 0 and I patientscan be recommended for standard of care with minimal added treatmentcosts. A class II, IIa and IIb patient would be of medium joint specificrisk (e.g., a weighting of 1.3 to 1.5 as compared to a typical patient).Surgery would not be recommended and treatment of the CA or RA would berecommended. If surgery is warranted, additional options such asprophylactic antibiotic/anti-inflammatory administration and proactivelong-term monitoring may be recommended for Class II, Ha, and IIbpatients. A class III, IIIa and IIIB patient would be of medium-highjoint specific risk (e.g., a weighting of 1.6 to 1.8 as compared to atypical patient). Further diagnosis for possible septic arthritis wouldnot be recommended and treatment with antibiotics would be recommended.A class III patient should not be subject to surgery until furthertreatment and diagnosis is completed. A class IV, IVa and IVb patientwould be high joint specific risk of a potential poor outcome and/orcomplication (e.g. a weighting above 1.8 as compared to the typicalpatient). Treatment for septic arthritis would be recommended. Theseweighting along with other data (e.g., classification can be inputelectronically into a patient risk calculator system as furtherdiscussed in FIG. 3.

FIG. 3 shows a system 300 whereby data from the system 100 such as theresults data of the RISC panel algorithm are electronically input to asecond system 302. The data can be scored, weighted, categorized, etc.at 304 as previously discussed prior to or at input in to the secondsystem 302. The system 302 can include a processor 306 and memory 308.The processor 306 can include any processing circuitry or software aspreviously described or discussed herein (e.g., controller, control suchas circuitry such as a printed circuit board (PCB), system on a chip(SoC), field-programmable gate array (FPGA), or other integrated circuitor hardware level types of applications). Similarly, the memory 308 caninclude any storage medium (e.g., database, volatile, non-volatile,magnetic, optical, or electrical media, such as a random access memory(RAM), read-only memory (ROM), non-volatile RAM (NVRAM),electrically-erasable programmable ROM (EEPROM), flash memory, or anyother digital media) as discussed herein.

The processor 306 can employ the memory 308 to retrieve instructionsthat are then executed on the processor 306. Thus, the processor 306 canstore and retrieve data or other information from analytics such asthose already executed by the RISC panel device on joint specific data,as well as data and other information related to patient populationmodeling.

The memory 308 can include data 310 representative of a plurality ofcharacteristics that are considered in determining the suitability ofthe patient for surgery including sample results data (e.g.,characteristic/category/class based upon input from the RISC panelalgorithm. The sample results data can be joint-specific inflammationdata as discussed previously.

The processor 306 can determine 312 a likely patient outcome based uponthe characteristics including the sample data input that is jointspecific regarding inflammation type from the RISC panel algorithm.Further characteristics that can be considered in determining patientoutcome likelihood can include any one or combination of: surgery type,anaesthesia level, age, lifestyle, pre-operative activity level,Body-Mass-Index (BMI), patient clinical history (e.g., co-morbidities,diseases, afflictions), clinical measures, patient reported outcomemeasures (PROM)s, willingness to attend a pre-operative educationalclass, patient recorded activities (e.g., sensor(s)), willingness toattend post-operative rehab such as physical therapy, surgery duration,patient ability to ambulate independently preoperatively without assistdevice, joint factors (bone loss, osteophyte size, deformity,soft-tissue envelop, etc.), existence of pre-exiting arthroplastyimplants, other characteristics.

A patient may be excluded from surgery purely on the basis of one ormore joint specific characteristics determined at the RISC panel level.Thus, the RISC panel assessment can be a primary factor in the treatmentthe patient receives according to some examples. As discussed, a classIII, MIIa, IIIb, or class IV, IVa, and IVb should be excluded fromsurgery on the basis of CA, possible septic arthritis or septicarthritis determination at the RISC panel level. Furthermore,combinations of characteristics could exclude a patient from surgery aspatient outcome would be undesirable for one or more parties to thesurgery.

The system 300 can operate with one or more levels having differentprocessing power, processing capabilities, time limits, or the like.Devices described can be wearable devices. However, further devices caninclude a mobile phone, tablet, computer (e.g., desktop or laptop),cloud-based devices (e.g., a server), which may include access to adatabase, or the like. The system 300 can utilize large population datasets. Compiled data that is stripped of personally identifiableinformation may be used with data from other users. Different models maybe developed (e.g., for different user populations, for differentsurgeries, for different user timelines, for different user diagnoses,etc.). Predictive analytics may be used to drive a change in the system300 dynamically. For example, an output of a model run on the system 300may be used to change a parameter/characteristic, such as the type ofinflammatory arthritis experienced by the patient. The system 300 maychange patient outcome based upon such change in characteristic(s).

FIG. 4 shows a method 400 implemented by the system 300 of FIG. 3. Themethod 400 can include electronically outputting 402 from the RISC panelalgorithm a determination including the sample result data. Asdiscussed, the result data can be a type of inflammatory arthritis, acategory (e.g., classification as discussed), a severity ofinflammation, a treatment, and/or a weighting based upon theinflammation type, severity, etc.). The method 400 can use the sampleresult data (which is joint specific) as an input 404 to a riskcalculation model to generate a predicted outcome (e.g., likelihood ofcomplication) for the patient. The method 400 can output for display ona user interface 406 information related to the patient such as amedical intervention (operate, do not operate, in-patient, out-patient,treatment recommendation, risk score (e.g., a statistical likelihood ofpatient complication) that is based upon the predicted outcome.

FIG. 5 illustrates a block diagram of an example machine 500 upon whichany one or more of the techniques discussed herein may perform inaccordance with some embodiments. This example machine can operate someor all of the systems discussed herein. In some example, the system 100or system 300 can operate on the example machine 500. In other examples,the example machine 500 is merely one of many such machines utilized tooperate the system. In alternative embodiments, the machine 500 mayoperate as a standalone device or may be connected (e.g., networked) toother machines. In a networked deployment, the machine 500 may operatein the capacity of a server machine, a client machine, or both inserver-client network environments. In an example, the machine 500 mayact as a peer machine in peer-to-peer (P2P) (or other distributed)network environment. The machine 500 may be a personal computer (PC), atablet PC, a set-top box (STB), a personal digital assistant (PDA), amobile telephone, a web appliance, a network router, switch or bridge,or any machine capable of executing instructions (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute a set (or multiple sets) of instructions to perform anyone or more of the methodologies discussed herein, such as cloudcomputing, software as a service (SaaS), other computer clusterconfigurations.

Machine (e.g., computer system) 500 may include a hardware processor 502(e.g., a central processing unit (CPU), a graphics processing unit(GPU), a hardware processor core, or any combination thereof), a mainmemory 504 and a static memory 506, some or all of which may communicatewith each other via an interlink (e.g., bus) 508. The machine 500 mayfurther include a display unit 510, an alphanumeric input device 512(e.g., a keyboard), and a user interface (UI) navigation device 514(e.g., a mouse). In an example, the display unit 510, input device 512and UI navigation device 514 may be a touch screen display. The machine500 may additionally include a storage device (e.g., drive unit) 516, asignal generation device 518 (e.g., a speaker), a network interfacedevice 520, and plurality of sensors 521, such as any of those discussedpreviously (e.g., an IMU, a global positioning system (GPS) sensor,compass, accelerometer, or other sensor). The machine 500 may include anoutput controller 528, such as a serial (e.g., Universal Serial Bus(USB), parallel, or other wired or wireless (e.g., infrared (IR), nearfield communication (NFC), etc.) connection to communicate or controlone or more peripheral devices (e.g., a printer, card reader, etc.).

The storage device 516 may include a machine readable medium 522 onwhich is stored one or more sets of data structures or instructions 524(e.g., software) embodying or utilized by any one or more of thetechniques or functions described herein. The instructions 524 may alsoreside, completely or at least partially, within the main memory 504,within static memory 506, or within the hardware processor 502 duringexecution thereof by the machine 500. In an example, one or anycombination of the hardware processor 502, the main memory 504, thestatic memory 506, or the storage device 516 may constitute machinereadable media.

While the machine readable medium 522 is illustrated as a single medium,the term “machine readable medium” may include a single medium ormultiple media (e.g., a centralized or distributed database, and/orassociated caches and servers) configured to store the one or moreinstructions 524. The term “machine readable medium” may include anymedium that is capable of storing, encoding, or carrying instructionsfor execution by the machine 500 and that cause the machine 500 toperform any one or more of the techniques of the present disclosure, orthat is capable of storing, encoding or carrying data structures used byor associated with such instructions. Non-limiting machine-readablemedium examples may include solid-state memories, and optical andmagnetic media.

The instructions 524 may further be transmitted or received over acommunications network 526 using a transmission medium via the networkinterface device 520 utilizing any one of a number of transfer protocols(e.g., frame relay, internet protocol (IP), transmission controlprotocol (TCP), user datagram protocol (UDP), hypertext transferprotocol (HTTP), etc.). Example communication networks may include alocal area network (LAN), a wide area network (WAN), a packet datanetwork (e.g., the Internet), mobile telephone networks (e.g., cellularnetworks), Plain Old Telephone (POTS) networks, and wireless datanetworks (e.g., Institute of Electrical and Electronics Engineers (IEEE)802.11 family of standards known as Wi-Fi®, IEEE 802.16 family ofstandards known as WiMax®), IEEE 802.15.4 family of standards,peer-to-peer (P2P) networks, among others. In an example, the networkinterface device 520 may include one or more physical jacks (e.g.,Ethernet, coaxial, or phone jacks) or one or more antennas to connect tothe communications network 526. In an example, the network interfacedevice 520 may include a plurality of antennas to wirelessly communicateusing at least one of single-input multiple-output (SIMO),multiple-input multiple-output (MIMO), or multiple-input single-output(MISO) techniques. The term “transmission medium” shall be taken toinclude any intangible medium that is capable of storing, encoding orcarrying instructions for execution by the machine 500, and includesdigital or analog communications signals or other intangible medium tofacilitate communication of such software.

A brief reference to various possible examples related to the claims andembodiments is provided. These examples are referenced as aspects andtechniques.

In some aspects, the techniques described herein relate to a method ofelectronically diagnosing a cause of an inflamed and/or painful joint ofa patient using a joint specific biological material, the methodincluding: receiving, using an electronic device data regarding testsperformed on the joint specific biological material; determining withthe electronic device if osteoarthritis (OA) is the cause of theinflamed and/or painful joint based upon one or more of the tests,wherein the diagnosing is based upon a level of cartilage oligomericmatrix protein (COMP) and a ratio of COMP to interleukin-8 (IL-8) in thejoint specific biological material; if the one or more of the testsindicate OA is not the cause of the inflamed joint, determining with theelectronic device if inflammatory arthritis, crystalline arthritis,rheumatoid arthritis, possible septic arthritis or septic arthritis isthe cause of the inflamed joint based upon a further plurality of thetests; and generating with the electronic device a sample results reportwith result data including diagnosis for use by a clinician.

In some aspects, the techniques described herein relate to a method,wherein generating with the electronic device the sample results reportincludes a differential diagnosis of arthritis for the patient.

In some aspects, the techniques described herein relate to a method,wherein the determining the inflammatory arthritis, crystallinearthritis, rheumatoid arthritis, or septic arthritis is based uponpresence of or absence of monosodium urate (MSU) crystals or calciumpyrophosphate dihydrate (CPPD) crystals in the joint specific biologicalmaterial, the presence or absence of Immunoglobulin G (IgG) antibodiesto citrullinated peptide (Anti-CCP), presence of absence of rheumatoidfactor (RF), and by white blood cell (WBC) count and differential in thejoint specific biological material.

In some aspects, the techniques described herein relate to a method,wherein the determining one of septic arthritis, inflammatory arthritis,and possible septic arthritis is by WBC count and/or percentage ofpolymorphonuclear cells (% PMN) in the joint specific biologicalmaterial.

In some aspects, the techniques described herein relate to a method,wherein a result of WBC >3000 cells/μL and/or % PMN >70 is indicative ofseptic arthritis or possible septic arthritis.

In some aspects, the techniques described herein relate to a method,wherein the possible septic arthritis is determined by results ofWBC >3000 cells/μL and/or % PMN >70, COMP/IL-8 ratio <4.3, negative fornative septic arthritis (alpha defensin and lactate), negative formicrobial ID, and negative for microbial culture in the joint specificbiological material.

In some aspects, the techniques described herein relate to a method,wherein the septic arthritis is determined by results of WBC >3000cells/μL and/or % PMN >70, COMP/IL-8 ratio <4.3, and at least one of:positive for native septic arthritis (alpha defensin and lactate),positive for microbial ID, or positive for microbial culture in thejoint specific biological material.

In some aspects, the techniques described herein relate to a method,wherein the inflammatory arthritis is determined by absence ofmonosodium urate (MSU) crystals, absence of calcium pyrophosphatedihydrate (CPPD) crystals, absence of Anti-CCP and RF, COMP/IL-8 ratio<4.3 and WBC >2000 cells/μL and/or % PMN >70.

In some aspects, the techniques described herein relate to a method,further including categorizing the diagnosis according to one of aplurality of classes according to at least one of a level or type ofinflammation, wherein the plurality of classes correspond to thediagnosis of OA, inflammatory arthritis, crystalline arthritis,rheumatoid arthritis, and septic arthritis.

In some aspects, the techniques described herein relate to a method,wherein the classes include subclassification related to presence ofmonosodium urate (MSU) crystals, presence of calcium pyrophosphatedihydrate (CPPD) crystals, presence of Anti-CCP, rheumatoid factor, andby one or more of a culture of the joint specific biological material, amicrobial ID, or a presence or absence of alpha-defensin (AD) andL-lactate.

In some aspects, the techniques described herein relate to a method,further including electronically communicating the result data as aninput to a pre-operative patient risk stratification tool andelectronically determining with the patient risk stratification tool apredicted post-surgical patient outcome or risk based upon the resultdata.

In some aspects, the techniques described herein relate to a method,further including weighting the predicted patient outcome according tothe one of several classes.

In some aspects, the techniques described herein relate to a method,wherein the categorizing the diagnosis according to the one of severalclasses is according to at least one of a type of inflammation or aninflammatory severity in addition to arthritic type.

In some aspects, the techniques described herein relate to anelectronically implemented system for diagnosing a cause of an inflamedand/or painful joint of a patient using a joint specific biologicalmaterial, the system including: processing circuitry; and a memory thatincludes instructions, the instructions, when executed by the processingcircuitry, cause the processing circuitry to: receive data regardingtests performed on the joint specific biological material; determine ifosteoarthritis (OA) is the cause of the inflamed and/or painful jointbased upon one or more of the tests, wherein the diagnosing is basedupon a level of cartilage oligomeric matrix protein (COMP) and a ratioof COMP to interleukin-8 (IL-8) in the joint specific biologicalmaterial; determine, if the one more of the tests indicate OA is not thecause of the inflamed joint, if inflammatory arthritis, crystallinearthritis, rheumatoid arthritis, possible septic arthritis or septicarthritis is the cause of the inflamed joint based upon a furtherplurality of the tests; and generate result data including jointspecific diagnosis for use by a clinician.

In some aspects, the techniques described herein relate to a system,wherein the determination of the inflammatory arthritis, crystallinearthritis, rheumatoid arthritis, or septic arthritis is based uponpresence of or absence of monosodium urate (MSU) crystals or calciumpyrophosphate dihydrate (CPPD) crystals in the joint specific biologicalmaterial, the presence or absence of anti-cyclic citrullinated peptide,rheumatoid factor, and by a white blood cell (WBC) count and apercentage of polymorphonuclear cells (% PMN) in the joint specificbiological material.

In some aspects, the techniques described herein relate to a system,wherein the instructions, when executed by the processing circuitry,cause the processing circuitry to determine one of the septic arthritis,the inflammatory arthritis and the possible septic arthritis is by WBCcount or percentage of polymorphonuclear WBCs (% PMN) in the jointspecific biological material.

In some aspects, the techniques described herein relate to a system,wherein a result of WBC >3000 cells/μL and/or % PMN >70 is indicative ofseptic arthritis or possible septic arthritis.

In some aspects, the techniques described herein relate to a system,wherein possible septic arthritis is determined by the result WBC >3000cells/μL and/or % PMN >70, and COMP/IL-8 ratio <4.3, negative for nativeseptic arthritis, negative for microbial ID, and negative for microbialculture in the joint specific biological material and the septicarthritis is determined by determined by the ratio of WBC >3000 cells/pL and/or % PMN >70, COMP/IL-8 ratio <4.3, and at least one of: positivefor native septic arthritis (alpha defensin and lactate), positive formicrobial ID, or positive for microbial culture in the joint specificbiological material.

In some aspects, the techniques described herein relate to a system,wherein a non-specific inflammatory arthritis is determined by absenceof monosodium urate (MSU) crystals, absence of calcium pyrophosphatedihydrate (CPPD) crystals, absence of anti-cyclic citrullinated peptide,absence of RF, COMP/IL-8 ratio <4.3, WBC ≤3000, and % PMN <70.

In some aspects, the techniques described herein relate to a system,wherein the instructions, when executed by the processing circuitry,cause the processing circuitry to categorize the diagnosis according toone of a plurality of classes according to a level of inflammation,wherein the plurality of classes correspond to the diagnosis of OA,inflammatory arthritis, crystalline arthritis, rheumatoid arthritis, andseptic arthritis.

In some aspects, the techniques described herein relate to a system,wherein the classes include subclassification related to presence orabsence of monosodium urate (MSU) crystals, presence or absence ofcalcium pyrophosphate dihydrate (CPPD) crystals, presence or absence ofanti-cyclic citrullinated peptide, presence of absence of rheumatoidfactor, WBC count and differential, and by one or more of a culture ofthe joint specific biological material, positive identification ofcausative organism by microbial ID, or a presence or absence ofalpha-defensin (AD) and L-lactate.

In some aspects, the techniques described herein relate to a system,further including: a second system including: processing circuitry; anda memory that includes instructions, the instructions, when executed bythe processing circuitry, cause the processing circuitry to: communicatewith the system to retrieve the result data; and determine, according toa risk stratification tool a predicted patient outcome based upon theresult data.

In some aspects, the techniques described herein relate to a system,wherein the instructions, when executed by the processing circuitry,cause the processing circuitry to weight the predicted patient outcomeaccording to the one of several classes.

In some aspects, the techniques described herein relate to a method ofelectronically assessing a likelihood of an outcome for a patientexperiencing an inflamed joint, the method including: determining, witha computing device, if an inflamed joint of the patient is caused byosteoarthritis (OA) inflammatory arthritis, crystalline arthritis,rheumatoid arthritis, possible septic arthritis or septic arthritisbased upon tests performed on a joint specific biological material ofthe patient; generating results data from the determining; categorizingand weighting the results data; and performing a risk analysis on theresults data the risk analysis that accounts for one or more of thecategorizing and weighting of the results data in accessing the patientoutcome.

In some aspects, the techniques described herein relate to amachine-readable medium including instructions that, when executed byprocessing circuitry, cause the processing circuitry to performoperations to implement elements.

In some aspects, the techniques described herein relate to an apparatusincluding means to implement elements.

In some aspects, the techniques described herein relate to a system toimplement elements.

In some aspects, the techniques described herein relate to the method,system, machine-readable medium, or apparatus including any elementsabove.

The various aspects and techniques describe can include or use, or canoptionally be combined with any portion or combination of any portionsof any one or more of the aspects or techniques to include or use,subject matter that can include means for performing any one or more ofthe functions of various systems, apparatus, method, or amachine-readable medium including instructions that, when performed by amachine, cause the machine to perform any one or more of the functions.

Method examples described herein can be machine or computer-implementedat least in part. Some examples can include a computer-readable mediumor machine-readable medium encoded with instructions operable toconfigure an electronic device to perform methods as described in theabove examples, including one or more of the algorithms described inabove Examples. An implementation of such methods can include code, suchas microcode, assembly language code, a higher-level language code, orthe like. Such code can include computer readable instructions forperforming various methods. The code may form portions of computerprogram products. A code can be tangibly stored on one or more volatile,non-transitory, or non-volatile tangible computer-readable media, suchas during execution or at other times. Examples of these tangiblecomputer-readable media can include, but are not limited to, hard disks,removable magnetic disks, removable optical disks (e.g., compact disksand digital video disks), magnetic cassettes, memory cards or sticks,random access memories (RAMs), read only memories (ROMs), and the like.

What is claimed is:
 1. A method of electronically diagnosing a cause ofan inflamed and/or painful joint of a patient using a joint specificbiological material, the method comprising: receiving, using anelectronic device data regarding tests performed on the joint specificbiological material; determining with the electronic device ifosteoarthritis (OA) is the cause of the inflamed and/or painful jointbased upon one or more of the tests, wherein the diagnosing is basedupon a level of cartilage oligomeric matrix protein (COMP) and a ratioof COMP to interleukin-8 (IL-8) in the joint specific biologicalmaterial; if the one or more of the tests indicate OA is not the causeof the inflamed joint, determining with the electronic device ifinflammatory arthritis, crystalline arthritis, rheumatoid arthritis,possible septic arthritis or septic arthritis is the cause of theinflamed joint based upon a further plurality of the tests; andgenerating with the electronic device a sample results report withresult data including diagnosis for use by a clinician.
 2. The method ofclaim 1, wherein generating with the electronic device the sampleresults report includes a differential diagnosis of arthritis for thepatient.
 3. The method of claim 1, wherein the determining theinflammatory arthritis, crystalline arthritis, rheumatoid arthritis, orseptic arthritis is based upon presence of or absence of monosodiumurate (MSU) crystals or calcium pyrophosphate dihydrate (CPPD) crystalsin the joint specific biological material, the presence or absence ofImmunoglobulin G (IgG) antibodies to citrullinated peptide (Anti-CCP),presence of absence of rheumatoid factor (RF), and by white blood cell(WBC) count and differential in the joint specific biological material.4. The method of claim 3, wherein the determining one of septicarthritis, inflammatory arthritis, and possible septic arthritis is byWBC count and percentage of polymorphonuclear cells (% PMN) in the jointspecific biological material.
 5. The method of claim 4, wherein aresults of WBC >3000 cells/μL and/or % PMN >70 is indicative of septicarthritis or possible septic arthritis.
 6. The method of claim 4,wherein the possible septic arthritis is determined by results ofWBC >3000 cells/μL and/or % PMN >70, COMP/IL-8 ratio <4.3, negative fornative septic arthritis (alpha defensin and lactate), negative formicrobial ID, and negative for microbial culture in the joint specificbiological material.
 7. The method of claim 4, wherein the septicarthritis is determined by results of WBC >3000 cells/μL and/or %PMN >70, COMP/IL-8 ratio <4.3, and at least one of: positive for nativeseptic arthritis (alpha defensin and lactate), positive for microbialID, or positive for microbial culture in the joint specific biologicalmaterial.
 8. The method of claim 4, wherein the inflammatory arthritisis determined by absence of monosodium urate (MSU) crystals, absence ofcalcium pyrophosphate dihydrate (CPPD) crystals, absence of Anti-CCP andRF, COMP/IL-8 ratio <4.3 and WBC >3000 cells/μL or % PMN >70.
 9. Themethod of claim 1, further comprising categorizing the diagnosisaccording to one of a plurality of classes according to at least one ofa level or type of inflammation, wherein the plurality of classescorrespond to the diagnosis of OA, inflammatory arthritis, crystallinearthritis, rheumatoid arthritis, and septic arthritis.
 10. The method ofclaim 9, wherein the classes include subclassification related topresence of monosodium urate (MSU) crystals, presence of calciumpyrophosphate dihydrate (CPPD) crystals, presence of Anti-CCP,rheumatoid factor, and by one or more of a culture of the joint specificbiological material, a microbial ID, or a presence or absence ofalpha-defensin (AD) and L-lactate.
 11. The method of claim 9, furthercomprising electronically communicating the result data as an input to apre-operative patient risk stratification tool and electronicallydetermining with the patient risk stratification tool a predictedpost-surgical patient outcome or risk based upon the result data. 12.The method of claim 10, further comprising weighting the predictedpatient outcome according to the one of several classes.
 13. The methodof claim 8, wherein the categorizing the diagnosis according to the oneof several classes is according to at least one of a type ofinflammation or an inflammatory severity in addition to arthritic type.14. An electronically implemented system for diagnosing a cause of aninflamed and/or painful joint of a patient using a joint specificbiological material, the system comprising: processing circuitry; and amemory that includes instructions, the instructions, when executed bythe processing circuitry, cause the processing circuitry to: receivedata regarding tests performed on the joint specific biologicalmaterial; determine if osteoarthritis (OA) is the cause of the inflamedand/or painful joint based upon one or more of the tests, wherein thediagnosing is based upon a level of cartilage oligomeric matrix protein(COMP) and a ratio of COMP to interleukin-8 (IL-8) in the joint specificbiological material; determine, if the one more of the tests indicate OAis not the cause of the inflamed joint, if inflammatory arthritis,crystalline arthritis, rheumatoid arthritis, possible septic arthritisor septic arthritis is the cause of the inflamed joint based upon afurther plurality of the tests; and generate result data including jointspecific diagnosis for use by a clinician.
 15. The system of claim 14,wherein the determination of the inflammatory arthritis, crystallinearthritis, rheumatoid arthritis, or septic arthritis is based uponpresence of or absence of monosodium urate (MSU) crystals or calciumpyrophosphate dihydrate (CPPD) crystals in the joint specific biologicalmaterial, the presence or absence of anti-cyclic citrullinated peptide,rheumatoid factor, and by a white blood cell (WBC) count and apercentage of polymorphonuclear cells (% PMN) in the joint specificbiological material.
 16. The system of claim 15, wherein theinstructions, when executed by the processing circuitry, cause theprocessing circuitry to determine one of the septic arthritis, theinflammatory arthritis and the possible septic arthritis is by WBC countor percentage of polymorphonuclear WBCs (% PMN) in the joint specificbiological material.
 17. The system of claim 16, wherein a result ofWBC >3000 cells/μL and/or % PMN >70 is indicative of septic arthritis orpossible septic arthritis.
 18. The system of claim 17, wherein thepossible septic arthritis is determined by the result WBC >3000 cells/μLand/or % PMN >70, and COMP/IL-8 ratio <4.3, negative for native septicarthritis, negative for microbial ID, and negative for microbial culturein the joint specific biological material and the septic arthritis isdetermined by determined by the ratio of WBC >3000 cells/μL and/or %PMN >70, COMP/IL-8 ratio <4.3, and at least one of: positive for nativeseptic arthritis (alpha defensin and lactate), positive for microbialID, or positive for microbial culture in the joint specific biologicalmaterial.
 19. The system of claim 17, wherein a non-specificinflammatory arthritis is determined by absence of monosodium urate(MSU) crystals, absence of calcium pyrophosphate dihydrate (CPPD)crystals, absence of anti-cyclic citrullinated peptide, absence of RF,COMP/IL-8 ratio <4.3, WBC ≤3000, and % PMN <70.
 20. The system of claim14, wherein the instructions, when executed by the processing circuitry,cause the processing circuitry to categorize the diagnosis according toone of a plurality of classes according to a level of inflammation,wherein the plurality of classes correspond to the diagnosis of OA,inflammatory arthritis, crystalline arthritis, rheumatoid arthritis, andseptic arthritis.
 21. The system of claim 20, wherein the classesinclude subclassification related to presence or absence of monosodiumurate (MSU) crystals, presence or absence of calcium pyrophosphatedihydrate (CPPD) crystals, presence or absence of anti-cycliccitrullinated peptide, presence of absence of rheumatoid factor, WBCcount and differential, and by one or more of a culture of the jointspecific biological material, positive identification of causativeorganism by microbial ID, or a presence or absence of alpha-defensin(AD) and L-lactate.
 22. The system of claim 21, further comprising: asecond system including: processing circuitry; and a memory thatincludes instructions, the instructions, when executed by the processingcircuitry, cause the processing circuitry to: communicate with thesystem to retrieve the result data; and determine, according to a riskstratification tool a predicted patient outcome based upon the resultdata.
 23. The system of claim 22, wherein the instructions, whenexecuted by the processing circuitry, cause the processing circuitry toweight the predicted patient outcome according to the one of severalclasses.
 24. A method of electronically assessing a likelihood of anoutcome for a patient experiencing an inflamed joint, the methodcomprising: determining, with a computing device, if an inflamed jointof the patient is caused by osteoarthritis (OA) inflammatory arthritis,crystalline arthritis, rheumatoid arthritis, possible septic arthritisor septic arthritis based upon tests performed on a joint specificbiological material of the patient; generating results data from thedetermining; categorizing and weighting the results data; and performinga risk analysis on the results data the risk analysis that accounts forone or more of the categorizing and weighting of the results data inaccessing the patient outcome.